Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods.

Mixed models for short-run forecasting of electricity prices: Application for the Spanish market

Short-run forecasting of electricity prices has become necessary for power generation unit schedule, since it is the basis of every profit maximization strategy. In this article a new and very easy method to compute accurate forecasts for electricity prices using mixed models is proposed. The main idea is to develop an efficient tool for one-step-ahead forecasting in the future, combining several prediction methods for which forecasting performance has been checked and compared for a span of several years. Also as a novelty, the 24 hourly time series has been modelled separately, instead of the complete time series of the prices. This allows one to take advantage of the homogeneity of these 24 time series. The purpose of this paper is to select the model that leads to smaller prediction errors and to obtain the appropriate length of time to use for forecasting. These results have been obtained by means of a computational experiment. A mixed model which combines the advantages of the two new models discussed is proposed. Some numerical results for the Spanish market are shown, but this new methodology can be applied to other electricity markets as well

Stability Analysis of a Run-of-River Diversion Hydropower Plant With Surge Tank And Spillway in the Head Pond

Run-of-river hydropower plants usually lack significant storage capacity; therefore, the more adequate control strategy would consist of keeping a constant water level at the intake pond in order to harness the maximum amount of energy from the river flow or to reduce the surface flooded in the head pond. In this paper, a standard PI control system of a run-of-river diversion hydropower plant with surge tank and a spillway in the head pond that evacuates part of the river flow plant is studied. A stability analysis based on the Routh-Hurwitz criterion is carried out and a practical criterion for tuning the gains of the PI controller is proposed. Conclusions about the head pond and surge tank areas are drawn from the stability analysis. Finally, this criterion is applied to a real hydropower plant in design state; the importance of considering the spillway dimensions and turbine characteristic curves for adequate tuning of the controller gains is highlighted